Dans-DiscountModels/12b-mn-dans-personality-engine-v1.3.0-TestArticle-1
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.10.0.dev0
base_model: Dans-DiscountModels/Mistral-Nemo-Base-2407-DanChat
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code:
# wandb configuration
wandb_project: 12b-mn-dans-personality-engine
wandb_watch:
wandb_run_id: V1.3.0-1-4 # V{Version}-{Run Number}-{Attempt Number}
wandb_log_model:
# push checkpoints to hub
hub_model_id: Dans-DiscountModels/12b-mn-dans-personality-engine-v1.3.0-TestArticle-1
# how to push checkpoints to hub
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy: "every_save"
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: true
# where to save the finished model to
output_dir: ./12b-mn-dans-personality-engine-v1.3.0
# dataset settings (local or huggingface repo)
datasets:
- path: Dans-DiscountModels/pretokenization-test-5
ds_type: parquet
type:
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
cut_cross_entropy: true
load_in_8bit: false
load_in_4bit: false
strict: false
adapter:
lora_model_dir:
dataset_prepared_path: ./12b-mn-dans-personality-engine-data
val_set_size: 0.003
sequence_len: 32768
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
gradient_checkpointing: true
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: ademamix_8bit
optim_args: "beta1=0.9,beta2=0.999,beta3=0.999,alpha=5"
lr_scheduler: rex
learning_rate: 0.00001
cosine_min_lr_ratio:
weight_decay:
max_grad_norm: 0.001
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 24
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 2
save_total_limit: 1
debug: false
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
special_tokens:
</details><br>
12b-mn-dans-personality-engine-v1.3.0-TestArticle-1
This model is a fine-tuned version of Dans-DiscountModels/Mistral-Nemo-Base-2407-DanChat on the Dans-DiscountModels/pretokenization-test-5 dataset. It achieves the following results on the evaluation set:
- Loss: 1.4392
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- trainbatchsize: 2
- evalbatchsize: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradientaccumulationsteps: 2
- totaltrainbatch_size: 32
- totalevalbatch_size: 16
- optimizer: Use ademamix_8bit and the args are: beta1=0.9,beta2=0.999,beta3=0.999,alpha=5
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 321
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.4.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
